发表机构
City University of Hong Kong（香港城市大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
CANIS是一种类别无关的生成辅助3D标准化框架，借助图像-语义桥利用冻结图像转3D生成模型的先验，提升了下游3D任务性能，适用于不完整及真实世界扫描。
AI 中文摘要
将3D物体的方向标准化是3D理解与分析的基础。现有方法常依赖几何线索，但3D标准化最终需要语义层面有意义的方向。为解决这一差距，我们提出CANIS，这是一种类别无关、生成辅助的框架，它将冻结的图像转3D生成模型的语义方向先验引入3D标准化，无需针对标准化的特定训练或类别特定模板。具体而言，CANIS首先从候选视点渲染输入物体，选择一个信息丰富的视图，并以标准化方向生成一个代理。生成过程中，从输入编码的稀疏结构潜在变量引导代理保留物体的几何结构。随后，CANIS将所选图像用作输入与代理之间的语义桥：图像斑块识别代理上的语义区域，深度反向投影定位输入上的对应区域。得到的语义锚点约束几何匹配，据此我们估计出可将输入标准化的刚性变换。在合成基准上的实验验证了CANIS及其关键组件，对部分观测结果和OmniObject3D的定性结果表明其适用于不完整和真实世界的扫描。CANIS还提升了任意旋转下的下游3D分类、部件分割和密集对应任务的性能。项目页面：this https URL
英文摘要
Canonicalizing 3D object orientation is fundamental to 3D understanding and analysis. Existing approaches often rely on geometric cues, although 3D canonicalization ultimately requires a semantically meaningful orientation. To address this gap, we propose CANIS, a category-agnostic, generation-assisted framework that introduces the semantic orientation prior of a frozen image-to-3D generative model into 3D canonicalization, without canonicalization-specific training or category-specific templates. Specifically, CANIS first renders the input object from candidate viewpoints, selects an informative view, and generates a proxy in a canonical orientation. During generation, a sparse structural latent encoded from the input guides the proxy to preserve the geometry of an object. CANIS then uses the selected image as a semantic bridge between the input and the proxy. Image patches identify semantic regions on the proxy, and depth back-projection locates the corresponding regions on the input. The resulting semantic anchors constrain geometric matching, from which we estimate the rigid transformation that canonicalizes the input. Experiments on synthetic benchmarks validate CANIS and its key components, while qualitative results on partial observations and OmniObject3D suggest its applicability to incomplete and real-world scans. CANIS also improves downstream 3D classification, part segmentation, and dense correspondence under arbitrary rotations. Project page: https://kenkenzaii.github.io/Canis.
